Using data to improve public health: COVID-19 secondment

The COVID-19 Longitudinal Health and Wellbeing National Core Study (LH&W NCS) commissioned Thriva to provide an end-to-end serological testing service, sending antibody test kits to roughly 47,000 cohort participants from 11 longitudinal population studies between March and May 2021. These cohort members span age groups of 18-75yrs, socioeconomic status and ethnicity and the overall return rate was 68%. This testing coupled with linkage to vaccination data and electronic health records now allows the meaningful analysis of associations between cohort/life course data and antibody response. It will also allow us to add a key layer to case definition in longitudinal population-based studies. In addition to Thriva data across cohorts, there are other cohort resources which can be used to enrich analyses further. For example, the Avon Longitudinal Study of Parents and Children (ALSPAC) is part of the UK Coronavirus Immunology Consortium (UKCiC) and has a programme of work focused on understanding the evolution of the immune response. The study has collected over 100 symptomatic known cases, and matched these to 100 symptomatic controls (with no COVID-19 diagnosis), and 100 symptom free controls. Participants provided biosamples, and questionnaire and additional functional data during in-person appointments and extended immunological data are now available which will allow comparison to Thriva based data. We will utilise available longitudinal population cohort data (as above) and four-nation linked health records via the Data & Connectivity NCS to investigate the biological and societal factors underlying the differential immunological response to COVID-19 infection or vaccination. Objective 1: Evaluate factors associating with low antibody levels post vaccination. Preliminary analysis in the TwinsUK cohort has shown tentative signs of association between low antibody levels after vaccination and sociodemographic and mental health factors. We will investigate using logistic regression models whether these findings are replicated in other cohorts and whether other associations with pre- and trans- pandemic health, sociodemographics and natural infection status can be found. Objective 2: Investigate factors associated with differences between symptomatic and asymptomatic cases following natural infection. The Thriva antibody data provides a way to define and examine asymptomatic cases of COVID-19 when matched with symptom data within the NCS cohorts. Deep immunophenotyping was performed on ALSPAC-UKCiC data and has shown that there is a difference in response measured in antibody and T cell values between symptomatic and asymptomatic cases following natural infection. The question arises about the underlying factors that cause symptomatic versus asymptomatic COVID-19 infection. However, this data set is relatively small and using Thriva antibody data across the 11 cohorts could provide a stronger signal for these underlying factors. Objective 3: Examine biological explanations for variation in infection and vaccine immune response. Preliminary analysis in the ALSPAC cohort has highlighted that people with a self-reported weakened immune system have a weaker antibody response following first vaccination. Using data from ALSPAC, we will look for associations between a weaker antibody response to vaccination and HLA haplotype, PRS genotypic risk, history of infection, trajectories of health, epigenetic and metabolomic variation and other immune variables. Where possible, we will investigate whether these findings are replicated in other cohorts and whether there are central biological explanations to variable response. Further exploratory research questions: How to maximise return from any future rounds of Thriva data collection? Can additional methods such as Mendelian Randomisation and machine learning algorithms be used in the analyses to give further useful insights?